Purpose

We are interested if the same ordering of perturbations and regulated genes shows a similar pattern as that derived from processing the K562 essential screen data. That is, instead of calling new clusters on the GWPS and Rpe1 experiments, let’s assume the same clusters and see if they look coherent.

Initialization

Libraries

library(magrittr)
library(tidyverse)
library(pheatmap)
library(SummarizedExperiment)

Parameters

set.seed(20210818)

## input files
FILE_RD7_WUI="tbl/df_wui_rd7_essential_ui10.Rds"
FILE_KD8_WUI="tbl/df_wui_kd8_gwps_ui10.Rds"

CSV_TARGETS_KD6="tbl/df_target_nnclusters_kd6_essential_ui10.csv"
CSV_GENES_KD6="tbl/df_gene_nnclusters_kd6_essential_ui10.csv"
NULL_CLUSTERS=as.character(c(9,12,13))

idx_clusters_targets <- as.character(c(14,18,11,16,13,9,12,2,5,15,1,6,8,3,7,4,10,17))
idx_clusters_genes <- as.character(c(20,16,7,8,1,3,18,2,5,10,15,19,14,11,4,6,17))


## output files
FILE_OUT_RD7_FINAL="img/heatmap-rd7-essential-using-rd6-clusters.pdf"
FILE_OUT_KD8_FINAL="img/heatmap-kd8-gwps-using-rd6-clusters.pdf"

## aesthetics
NCOLORS=100
COLORS_BWR <- colorRampPalette(c("blue", "white", "red"))(NCOLORS)
COLORS_MKY <- colorRampPalette(c("magenta", "black", "yellow"))(NCOLORS)
COLORS_YKM <- colorRampPalette(c("yellow", "black", "magenta"))(NCOLORS)
COLORS_RKG <- colorRampPalette(c("red", "black", "green"))(NCOLORS)
breaks_dwui <- seq(-0.5, 0.5, length.out=NCOLORS + 1)
breaks_zdwui <- seq(-4, 4, length.out=NCOLORS + 1)
breaks_zdwui_broad <- seq(-6, 6, length.out=NCOLORS + 1)
breaks_pca <- seq(-20, 20, length.out=NCOLORS + 1)

RD7

Loading Data

df_targets_kd6 <- read_csv(CSV_TARGETS_KD6, col_types='cccc')
df_genes_kd6 <- read_csv(CSV_GENES_KD6, col_types='ccc')

df_wui_rd7 <- readRDS(FILE_RD7_WUI)

Preprocessing

sgid2gene <- df_wui_rd7 %>%
    dplyr::select(sgID_AB, target_gene) %>%
    distinct(sgID_AB, target_gene) %>%
    deframe()

ens2gene <- df_wui_rd7 %>%
    dplyr::select(gene_id, gene_name) %>%
    distinct(gene_id, gene_name) %>%
    deframe()

convert_rownames <- function (mat, in2out) {
    mat %>% set_rownames(in2out[rownames(.)])
}

convert_colnames <- function (mat, in2out) {
    mat %>% set_colnames(in2out[colnames(.)])
}

df_ntp_rd7 <- filter(df_wui_rd7, 
                     target_gene == "non-targeting",
                     gene_id %in% df_genes_kd6$gene_id) %>%
    group_by(gene_id, gene_name) %>%
    filter(!is.na(wui)) %>%
    summarize(mean_wui=weighted.mean(wui, n_cells), .groups='drop',
              sd_wui=sqrt(sum((wui-mean_wui)^2)/n()))

df_dwui_rd7 <- df_wui_rd7 %>%
    filter(sgID_AB %in% df_targets_kd6$sgID_AB,
           gene_id %in% df_genes_kd6$gene_id) %>%
    inner_join(df_ntp_rd7, by=c("gene_id", "gene_name")) %>%
    mutate(dwui=wui-mean_wui) %>%
    dplyr::select(gene_id, sgID_AB, wui, dwui)

wui_gene_target_rd7 <- df_dwui_rd7 %>%
    dplyr::select(gene_id, sgID_AB, wui) %>%
    pivot_wider(id_cols="gene_id", names_from="sgID_AB", values_from="wui") %>%
    column_to_rownames("gene_id") %>%
    as.matrix

dwui_gene_target_rd7 <- df_dwui_rd7 %>%
    dplyr::select(gene_id, sgID_AB, dwui) %>%
    pivot_wider(id_cols="gene_id", names_from="sgID_AB", values_from="dwui") %>%
    column_to_rownames("gene_id") %>%
    as.matrix 

zdwui_target_gene_rd7 <- t(dwui_gene_target_rd7) %>% scale(center=FALSE)

Clusters Indices

idx_genes <- df_genes_kd6 %>%
    filter(gene_id %in% colnames(zdwui_target_gene_rd7),
           cluster_id %in% idx_clusters_genes) %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
    arrange(cluster_id, gene_name) %$%
    gene_id

idx_genes_null <- df_genes_kd6 %>%
    filter(gene_id %in% colnames(zdwui_target_gene_rd7),
           cluster_id %in% NULL_CLUSTERS) %>%
    mutate(cluster_id=factor(cluster_id, levels=NULL_CLUSTERS)) %>%
    arrange(cluster_id, gene_name) %$%
    gene_id

idx_targets <- df_targets_kd6 %>%
    filter(sgID_AB %in% rownames(zdwui_target_gene_rd7)) %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
    arrange(cluster_id, target_gene) %$%
    sgID_AB

df_col_annots <- df_genes_kd6 %>%
    filter(gene_id %in% colnames(zdwui_target_gene_rd7),
           cluster_id %in% idx_clusters_genes) %>%
    dplyr::select(gene_id, cluster_id) %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
    dplyr::rename(gene_cluster=cluster_id) %>%
    column_to_rownames("gene_id")

df_row_annots <- df_targets_kd6 %>%
    filter(sgID_AB %in% rownames(zdwui_target_gene_rd7)) %>%
    dplyr::select(sgID_AB, cluster_id) %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
    dplyr::rename(target_cluster=cluster_id) %>%
    column_to_rownames("sgID_AB")

gaps_col <- df_genes_kd6 %>%
    filter(gene_id %in% colnames(zdwui_target_gene_rd7),
           cluster_id %in% idx_clusters_genes) %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
    arrange(cluster_id, gene_id) %$%
    table(cluster_id) %>%
    cumsum

gaps_row <- df_targets_kd6 %>%
    filter(sgID_AB %in% rownames(zdwui_target_gene_rd7)) %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
    arrange(cluster_id, sgID_AB) %$%
    table(cluster_id) %>%
    cumsum

Heatmap

pheatmap(zdwui_target_gene_rd7[idx_targets, idx_genes], 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=1, fontsize_row=10,
         annotation_row=df_row_annots, 
         annotation_col=df_col_annots,
         show_colnames=FALSE, show_rownames=FALSE, scale='none',
         annotation_names_row=FALSE, annotation_names_col=FALSE,
         gaps_row=gaps_row,
         gaps_col=gaps_col,
         cluster_rows=FALSE, cluster_cols=FALSE)

Export Plot

pheatmap(zdwui_target_gene_rd7[idx_targets, idx_genes], 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=1, fontsize_row=1,
         annotation_row=df_row_annots, 
         annotation_col=df_col_annots,
         show_colnames=TRUE, show_rownames=TRUE, scale='none',
         labels_row=sgid2gene[idx_targets],
         labels_col=ens2gene[idx_genes],
         annotation_names_row=FALSE, annotation_names_col=FALSE,
         gaps_row=gaps_row,
         gaps_col=gaps_col,
         cluster_rows=FALSE, cluster_cols=FALSE,
         filename=FILE_OUT_RD7_FINAL, width=16, height=16)

KD8 GWPS

Loading Data

df_wui_kd8 <- readRDS(FILE_KD8_WUI)

Preprocessing

sgid2gene <- df_wui_kd8 %>%
    dplyr::select(sgID_AB, target_gene) %>%
    distinct(sgID_AB, target_gene) %>%
    deframe()

ens2gene <- df_wui_kd8 %>%
    dplyr::select(gene_id, gene_name) %>%
    distinct(gene_id, gene_name) %>%
    deframe()

convert_rownames <- function (mat, in2out) {
    mat %>% set_rownames(in2out[rownames(.)])
}

convert_colnames <- function (mat, in2out) {
    mat %>% set_colnames(in2out[colnames(.)])
}

df_ntp_kd8 <- filter(df_wui_kd8, 
                     target_gene == "non-targeting",
                     gene_id %in% df_genes_kd6$gene_id) %>%
    group_by(gene_id, gene_name) %>%
    filter(!is.na(wui)) %>%
    summarize(mean_wui=weighted.mean(wui, n_cells), .groups='drop',
              sd_wui=sqrt(sum((wui-mean_wui)^2)/n()))

df_dwui_kd8 <- df_wui_kd8 %>%
    filter(sgID_AB %in% df_targets_kd6$sgID_AB,
           gene_id %in% df_genes_kd6$gene_id) %>%
    inner_join(df_ntp_kd8, by=c("gene_id", "gene_name")) %>%
    mutate(dwui=wui-mean_wui) %>%
    dplyr::select(gene_id, sgID_AB, wui, dwui)

wui_gene_target_kd8 <- df_dwui_kd8 %>%
    dplyr::select(gene_id, sgID_AB, wui) %>%
    pivot_wider(id_cols="gene_id", names_from="sgID_AB", values_from="wui") %>%
    column_to_rownames("gene_id") %>%
    as.matrix

dwui_gene_target_kd8 <- df_dwui_kd8 %>%
    dplyr::select(gene_id, sgID_AB, dwui) %>%
    pivot_wider(id_cols="gene_id", names_from="sgID_AB", values_from="dwui") %>%
    column_to_rownames("gene_id") %>%
    as.matrix 

zdwui_target_gene_kd8 <- t(dwui_gene_target_kd8) %>% scale(center=FALSE)

Clusters Indices

idx_genes <- df_genes_kd6 %>%
    filter(gene_id %in% colnames(zdwui_target_gene_kd8),
           cluster_id %in% idx_clusters_genes) %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
    arrange(cluster_id, gene_name) %$%
    gene_id

idx_genes_null <- df_genes_kd6 %>%
    filter(gene_id %in% colnames(zdwui_target_gene_kd8),
           cluster_id %in% NULL_CLUSTERS) %>%
    mutate(cluster_id=factor(cluster_id, levels=NULL_CLUSTERS)) %>%
    arrange(cluster_id, gene_name) %$%
    gene_id

idx_targets <- df_targets_kd6 %>%
    filter(sgID_AB %in% rownames(zdwui_target_gene_kd8)) %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
    arrange(cluster_id, target_gene) %$%
    sgID_AB

df_col_annots <- df_genes_kd6 %>%
    filter(gene_id %in% colnames(zdwui_target_gene_kd8),
           cluster_id %in% idx_clusters_genes) %>%
    dplyr::select(gene_id, cluster_id) %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
    dplyr::rename(gene_cluster=cluster_id) %>%
    column_to_rownames("gene_id")

df_row_annots <- df_targets_kd6 %>%
    filter(sgID_AB %in% rownames(zdwui_target_gene_kd8)) %>%
    dplyr::select(sgID_AB, cluster_id) %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
    dplyr::rename(target_cluster=cluster_id) %>%
    column_to_rownames("sgID_AB")

gaps_col <- df_genes_kd6 %>%
    filter(gene_id %in% colnames(zdwui_target_gene_kd8),
           cluster_id %in% idx_clusters_genes) %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_genes)) %>%
    arrange(cluster_id, gene_id) %$%
    table(cluster_id) %>%
    cumsum

gaps_row <- df_targets_kd6 %>%
    filter(sgID_AB %in% rownames(zdwui_target_gene_kd8)) %>%
    mutate(cluster_id=factor(cluster_id, levels=idx_clusters_targets)) %>%
    arrange(cluster_id, sgID_AB) %$%
    table(cluster_id) %>%
    cumsum

Heatmap

pheatmap(zdwui_target_gene_kd8[idx_targets, idx_genes], 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=1, fontsize_row=10,
         annotation_row=df_row_annots, 
         annotation_col=df_col_annots,
         show_colnames=FALSE, show_rownames=FALSE, scale='none',
         annotation_names_row=FALSE, annotation_names_col=FALSE,
         gaps_row=gaps_row,
         gaps_col=gaps_col,
         cluster_rows=FALSE, cluster_cols=FALSE)

Export Plot

pheatmap(zdwui_target_gene_kd8[idx_targets, idx_genes], 
         color=COLORS_BWR,
         breaks=breaks_zdwui,
         fontsize_col=1, fontsize_row=1,
         annotation_row=df_row_annots, 
         annotation_col=df_col_annots,
         show_colnames=TRUE, show_rownames=TRUE, scale='none',
         labels_row=sgid2gene[idx_targets],
         labels_col=ens2gene[idx_genes],
         annotation_names_row=FALSE, annotation_names_col=FALSE,
         gaps_row=gaps_row,
         gaps_col=gaps_col,
         cluster_rows=FALSE, cluster_cols=FALSE,
         filename=FILE_OUT_KD8_FINAL, width=16, height=16)

Runtime Details

Session Info

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS/LAPACK: /Users/mfansler/miniconda3/envs/bioc_3_14/lib/libopenblasp-r0.3.18.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] SummarizedExperiment_1.24.0 Biobase_2.54.0             
##  [3] GenomicRanges_1.46.0        GenomeInfoDb_1.30.0        
##  [5] IRanges_2.28.0              S4Vectors_0.32.0           
##  [7] BiocGenerics_0.40.0         MatrixGenerics_1.6.0       
##  [9] matrixStats_0.61.0          pheatmap_1.0.12            
## [11] forcats_0.5.1               stringr_1.4.0              
## [13] dplyr_1.0.8                 purrr_0.3.4                
## [15] readr_2.1.1                 tidyr_1.1.4                
## [17] tibble_3.1.7                ggplot2_3.3.5              
## [19] tidyverse_1.3.1             magrittr_2.0.3             
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-7           fs_1.5.2               lubridate_1.8.0       
##  [4] bit64_4.0.5            RColorBrewer_1.1-2     httr_1.4.2            
##  [7] tools_4.1.1            backports_1.4.0        bslib_0.3.1           
## [10] utf8_1.2.2             R6_2.5.1               DBI_1.1.1             
## [13] colorspace_2.0-2       withr_2.4.3            tidyselect_1.1.1      
## [16] bit_4.0.4              compiler_4.1.1         cli_3.3.0             
## [19] rvest_1.0.2            xml2_1.3.3             DelayedArray_0.20.0   
## [22] sass_0.4.0             scales_1.1.1           digest_0.6.29         
## [25] rmarkdown_2.11         XVector_0.34.0         pkgconfig_2.0.3       
## [28] htmltools_0.5.2        dbplyr_2.1.1           fastmap_1.1.0         
## [31] highr_0.9              rlang_1.0.2            readxl_1.3.1          
## [34] rstudioapi_0.13        jquerylib_0.1.4        generics_0.1.1        
## [37] farver_2.1.0           jsonlite_1.7.2         vroom_1.5.7           
## [40] RCurl_1.98-1.5         GenomeInfoDbData_1.2.7 Matrix_1.3-4          
## [43] Rcpp_1.0.7             munsell_0.5.0          fansi_0.5.0           
## [46] lifecycle_1.0.1        stringi_1.7.6          yaml_2.2.1            
## [49] zlibbioc_1.40.0        grid_4.1.1             parallel_4.1.1        
## [52] crayon_1.4.2           lattice_0.20-45        haven_2.4.3           
## [55] hms_1.1.1              knitr_1.39             pillar_1.7.0          
## [58] reprex_2.0.1           glue_1.6.2             evaluate_0.15         
## [61] modelr_0.1.8           vctrs_0.4.1            tzdb_0.2.0            
## [64] cellranger_1.1.0       gtable_0.3.0           assertthat_0.2.1      
## [67] xfun_0.30              broom_0.8.0            ellipsis_0.3.2

Conda Environment

## Conda Environment YAML
name: base
channels:
  - conda-forge
  - bioconda
  - defaults
dependencies:
  - anaconda-client=1.8.0=pyhd8ed1ab_0
  - anaconda-project=0.10.2=pyhd8ed1ab_0
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  - awscli=1.25.79=py39h6e9494a_0
  - backports=1.0=py_2
  - backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
  - backports.zoneinfo=0.2.1=py39h701faf5_5
  - bagit=1.8.1=pyhd8ed1ab_0
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  - beautifulsoup4=4.9.3=pyhb0f4dca_0
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  - ca-certificates=2022.9.24=h033912b_0
  - cachecontrol=0.12.11=pyhd8ed1ab_0
  - cairo=1.16.0=he43a7df_1008
  - cctools=973.0.1=hd9211c8_2
  - cctools_osx-64=973.0.1=h3e07e27_2
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  - cffi=1.15.1=py39hae9ecf2_0
  - chardet=5.0.0=py39h6e9494a_0
  - charset-normalizer=2.0.0=pyhd8ed1ab_0
  - click=8.1.3=py39h6e9494a_0
  - clyent=1.2.2=py_1
  - colorama=0.4.3=py_0
  - commonmark=0.9.1=py_0
  - conda=4.14.0=py39h6e9494a_0
  - conda-build=3.21.9=py39h6e9494a_1
  - conda-forge-pinning=2021.10.10.22.03.30=hd8ed1ab_0
  - conda-libmamba-solver=22.6.0=pyhd8ed1ab_0
  - conda-pack=0.6.0=pyhd3deb0d_0
  - conda-package-handling=1.9.0=py39ha30fb19_0
  - conda-smithy=3.17.2=pyhd8ed1ab_0
  - conda-standalone=4.10.3=h694c41f_0
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  - conda-suggest-conda-forge=2021.8.24=h694c41f_0
  - conda-verify=3.1.1=py39h6e9494a_1004
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  - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
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  - fonts-conda-ecosystem=1=0
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  - inotify_simple=1.3.5=pyha770c72_3
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  - isodate=0.6.0=py_1
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  - jinja2=3.0.1=pyhd8ed1ab_0
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  - jpeg=9d=hbcb3906_0
  - json5=0.9.5=pyh9f0ad1d_0
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  - ld64=609=hd2e7500_2
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  - ldid=2.1.2=h6a69015_3
  - lerc=2.2.1=h046ec9c_0
  - libarchive=3.5.2=h2b60450_1
  - libcurl=7.86.0=h57eb407_1
  - libcxx=14.0.6=hccf4f1f_0
  - libdeflate=1.10=h0d85af4_0
  - libedit=3.1.20191231=h0678c8f_2
  - libev=4.33=haf1e3a3_1
  - libffi=3.4.2=h0d85af4_5
  - libglib=2.70.2=hf1fb8c0_0
  - libiconv=1.17=hac89ed1_0
  - libidn2=2.3.2=h0d85af4_0
  - liblief=0.11.5=he49afe7_0
  - libllvm12=12.0.1=hd011deb_2
  - libmamba=1.0.0=h2bf831e_2
  - libmambapy=1.0.0=py39he069e75_2
  - libnghttp2=1.47.0=h7cbc4dc_1
  - libpng=1.6.37=h7cec526_2
  - librsvg=2.50.7=hd2a7919_0
  - libsolv=0.7.22=hd9580d2_0
  - libssh2=1.10.0=h7535e13_3
  - libtiff=4.3.0=h1167814_0
  - libunistring=0.9.10=h0d85af4_0
  - libwebp-base=1.2.1=h0d85af4_0
  - libxml2=2.9.12=h93ec3fd_0
  - libxslt=1.1.33=h5739fc3_2
  - libzlib=1.2.13=hfd90126_4
  - license-expression=1.2=py_0
  - lockfile=0.12.2=py_1
  - lxml=4.8.0=py39h63b48b0_2
  - lz4-c=1.9.3=he49afe7_1
  - lzo=2.10=haf1e3a3_1000
  - mamba=1.0.0=py39ha435c47_2
  - markupsafe=2.1.1=py39h63b48b0_1
  - msgpack-python=1.0.4=py39h92daf61_1
  - msrest=0.6.21=pyh44b312d_0
  - nbformat=5.1.3=pyhd8ed1ab_0
  - ncurses=6.3=h96cf925_1
  - nettle=3.6=hedd7734_0
  - oauthlib=3.1.1=pyhd8ed1ab_0
  - openssl=1.1.1s=hfd90126_0
  - pango=1.48.9=ha05cd14_0
  - patch=2.7.6=hbcf498f_1002
  - pcre=8.45=he49afe7_0
  - pcre2=10.37=ha16e1b2_0
  - perl=5.32.1=0_h0d85af4_perl5
  - pigz=2.6=h5dbffcc_0
  - pip=21.2.4=pyhd8ed1ab_0
  - pixman=0.40.0=hbcb3906_0
  - pkginfo=1.7.1=pyhd8ed1ab_0
  - popt=1.16=h7b079dc_2002
  - prompt-toolkit=3.0.20=pyha770c72_0
  - prompt_toolkit=3.0.20=hd8ed1ab_0
  - psutil=5.9.2=py39ha30fb19_0
  - py-lief=0.11.5=py39h9fcab8e_0
  - pyasn1=0.4.8=py_0
  - pybind11-abi=4=hd8ed1ab_3
  - pycosat=0.6.3=py39h63b48b0_1010
  - pycparser=2.20=pyh9f0ad1d_2
  - pycrypto=2.6.1=py39h89e85a6_1006
  - pygithub=1.53=py_0
  - pygments=2.10.0=pyhd8ed1ab_0
  - pyjwt=1.7.1=py_0
  - pyrsistent=0.18.1=py39h63b48b0_1
  - pysocks=1.7.1=pyha2e5f31_6
  - python=3.9.13=h57e37ff_0_cpython
  - python-dateutil=2.8.2=pyhd8ed1ab_0
  - python-libarchive-c=4.0=py39h6e9494a_1
  - python-tzdata=2021.5=pyhd8ed1ab_0
  - python_abi=3.9=2_cp39
  - pytz=2021.1=pyhd8ed1ab_0
  - pytz-deprecation-shim=0.1.0.post0=py39h6e9494a_2
  - pyyaml=5.4.1=py39h701faf5_3
  - readline=8.1.2=h3899abd_0
  - reproc=14.2.3=h0d85af4_0
  - reproc-cpp=14.2.3=he49afe7_0
  - requests=2.28.1=pyhd8ed1ab_1
  - requests-oauthlib=1.3.0=pyh9f0ad1d_0
  - rich=10.16.1=pyhd8ed1ab_0
  - ripgrep=13.0.0=h244e342_0
  - rsa=4.7.2=pyh44b312d_0
  - rsync=3.2.7=ha1fed10_0
  - ruamel.yaml=0.17.21=py39h63b48b0_1
  - ruamel.yaml.clib=0.2.6=py39h63b48b0_1
  - ruamel_yaml=0.15.80=py39h701faf5_1007
  - s3transfer=0.6.0=pyhd8ed1ab_0
  - scrypt=0.8.18=py39hbfd427f_4
  - setuptools=65.3.0=pyhd8ed1ab_1
  - six=1.16.0=pyh6c4a22f_0
  - smartmontools=7.2=h940c156_0
  - smmap=3.0.5=pyh44b312d_0
  - soupsieve=2.3.1=pyhd8ed1ab_0
  - sqlite=3.38.5=hd9f0692_0
  - tapi=1100.0.11=h9ce4665_0
  - tk=8.6.12=h5dbffcc_0
  - toolz=0.11.1=py_0
  - tornado=6.2=py39h701faf5_0
  - tqdm=4.62.2=pyhd8ed1ab_0
  - traitlets=5.1.0=pyhd8ed1ab_0
  - typing_extensions=3.10.0.0=pyha770c72_0
  - tzdata=2021e=he74cb21_0
  - tzlocal=4.2=py39h6e9494a_1
  - urllib3=1.26.6=pyhd8ed1ab_0
  - vsts-python-api=0.1.22=py_0
  - watchgod=0.7=pyhd8ed1ab_0
  - wcwidth=0.2.5=pyh9f0ad1d_2
  - wget=1.20.3=h52ee1ee_1
  - wheel=0.37.0=pyhd8ed1ab_1
  - wrapt=1.14.1=py39h701faf5_0
  - xxhash=0.8.0=h35c211d_3
  - xz=5.2.5=haf1e3a3_1
  - yaml=0.2.5=haf1e3a3_0
  - yaml-cpp=0.7.0=hb486fe8_1
  - zipp=3.5.0=pyhd8ed1ab_0
  - zlib=1.2.13=hfd90126_4
  - zstd=1.5.2=hfa58983_4
  - pip:
    - pyopenssl==20.0.1
prefix: /Users/mfansler/miniconda3